An algorithm to cluster data for efficient classification of support vector machines

Authors:
Der-Chiang Li;Yao-Hwei Fang
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan 701, Taiwan;Department of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan 701, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 6
Cited 3

Support-Vector Networks

Machine Learning
Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?

Machine Learning
Polynomial-Time Decomposition Algorithms for Support Vector Machines

Machine Learning
SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means

SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Multidimensional support vector machines for visualization of gene expression data

Bioinformatics

Increasing classification efficiency with multiple mirror classifiers

Expert Systems with Applications: An International Journal
Developing a semantic-enable information retrieval mechanism

Expert Systems with Applications: An International Journal
The data complexity index to construct an efficient cross-validation method

Decision Support Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency.